A large amount of sensitive data must remain accessible for decades or even centuries (e.g, electronic health records, governmental documents). Communicating such data over the Internet requires long-term secure communication channels, which, in turn, require robust key distribution protocols. Currently used key distribution protocols, however, are not designed for long-term security. Their security is either threatened by quantum computers, or, in principle, due to their reliance on computational problems. Quantum key distribution (QKD) protocols are information-theoretically secure and thereby offer long-term security against computational attacks. However, significant obstacles to their real-world use remain. This position paper, which is a multidisciplinary effort of computer scientists and physicists, systematizes knowledge about challenges of and strategies for realizing long-term secure Internet communication from QKD. We first analyze the performance and security of existing point-to-point QKD technology. Then, we discuss several approaches to enabling QKD in large-scale multi-user networks. Finally, we list important challenges that need to be addressed in order to make QKD-based long-term secure communication on the Internet practical.

Abstract An increasing amount of information today is generated, exchanged, and stored digitally. This also includes long-lived and highly sensitive information (e.g., electronic health records, governmental documents) whose integrity and confidentiality must be protected over decades or even centuries. While there is a vast amount of cryptography-based data protection schemes, only few are designed for long-term protection. Recently, Braun et al. (AsiaCCS’17) proposed the first long-term protection scheme that provides renewable integrity protection and information-theoretic confidentiality protection. However, computation and storage costs of their scheme increase significantly with the number of stored data items. As a result, their scheme appears suitable only for protecting databases with a small number of relatively large data items, but unsuitable for databases that hold a large number of relatively small data items (e.g., medical record databases).In this work, we present a solution for efficient long-term integrity and confidentiality protection of large datasets consisting of relatively small data items. First, we construct a renewable vector commitment scheme that is information-theoretically hiding under selective decommitment. We then combine this scheme with renewable timestamps and information-theoretically secure secret sharing. The resulting solution requires only a single timestamp for protecting a dataset while the state of the art requires a number of timestamps linear in the number of data items. Furthermore, we extend the scheme, that supports a single client, to a multi-client setting. Subsequently, we characterize the arising challenges with respect to integrity and confidentiality and discuss how our multi-client scheme tackles them. We implemented our solution and measured its performance in a scenario where 9600 data items are aggregated, stored, protected, and verified over a time span of 80 years. Our measurements show that our new solution completes this evaluation scenario an order of magnitude faster than the state of the art.

Abstract Genomic data is crucial in the understanding of many diseases and for the guidance of medical treatments. Pharmacogenomics and cancer genomics are just two areas in precision medicine of rapidly growing utilization. At the same time, whole-genome sequencing costs are plummeting below $ 1000, meaning that a rapid growth in full-genome data storage requirements is foreseeable. While privacy protection of genomic data is receiving growing attention, integrity protection of this long-lived and highly sensitive data much less so.We consider a scenario inspired by future pharmacogenomics, in which a patient’s genome data is stored over a long time period while random parts of it are periodically accessed by authorized parties such as doctors and clinicians. A protection scheme is described that preserves integrity of the genomic data in that scenario over a time horizon of 100 years. During such a long time period, cryptographic schemes will potentially break and therefore our scheme allows to update the integrity protection. Furthermore, integrity of parts of the genomic data can be verified without compromising the privacy of the remaining data. Finally, a performance evaluation and cost projection shows that privacy-preserving long-term integrity protection of genomic data is resource demanding, but in reach of current and future hardware technology and has negligible costs of storage.

The amount of digital data that requires long-term protection of integrity, authenticity, and confidentiality grows rapidly. Examples include electronic health records, genome data, and tax data. In this paper we present the secure storage system LINCOS, which provides protection of integrity, authenticity, and confidentiality in the long-term, i.e., for an indefinite time period. It is the first such system. It uses the long-term integrity scheme COPRIS, which is also presented here and is the first such scheme that does not leak any information about the protected data. COPRIS uses information-theoretic hiding commitments for confidentiality-preserving integrity and authenticity protection. LINCOS uses proactive secret sharing for confidential storage of secret data. We also present implementations of COPRIS and LINCOS. A special feature of our LINCOS implementation is the use of quantum key distribution and one-time pad encryption for information-theoretic private channels within the proactive secret sharing protocol. The technological platform for this is the Tokyo QKD Network, which is one of worlds most advanced networks of its kind. Our experimental evaluation establishes the feasibility of LINCOS and shows that in view of the expected progress in quantum communication technology, LINCOS is a promising solution for protecting very sensitive data in the cloud.

Current trends in technology, such as cloud computing, allow outsourcing the storage, backup, and archiving of data. This provides efficiency and flexibility, but also poses new risks for data security. It in particular became crucial to develop protection schemes that ensure security even in the long-term, i.e. beyond the lifetime of keys, certificates, and cryptographic primitives. However, all current solutions fail to provide optimal performance for different application scenarios. Thus, in this work, we present MoPS, a modular protection scheme to ensure authenticity and integrity for data stored over long periods of time. MoPS does not come with any requirements regarding the storage architecture and can therefore be used together with existing archiving or storage systems. It supports a set of techniques which can be plugged together, combined, and migrated in order to create customized solutions that fulfill the requirements of different application scenarios in the best possible way. As a proof of concept we implemented MoPS and provide performance measurements. Furthermore, our implementation provides additional features, such as guidance for non-expert users and export functionalities for external verifiers.

The amount of security critical information that is only available in digital form is increasing constantly. Some of these data, such as medical or tax data, need to be preserved for long periods of time. Thus, several schemes for long-term integrity protection of long-lived and archived data were developed. However, a comprehensive security analysis is still missing. In this paper we discuss existing security models for long lived systems and show to what extend they allow to prove the security of those schemes. Then, we introduce a new model that overcomes the shortcomings of the state of the art and allows to formally analyze timestamp-based long-term integrity schemes. Finally, we show how the security level of the long-term integrity scheme can be determined for concrete instantiations.

UNLABELLED: High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project. AVAILABILITY AND IMPLEMENTATION: The browser is available at http://www.uk10k.org/dalliance.html. Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv.